Journal of Infrastructure Systems

With average pipe replacement rates in the United States at about once in $200\text{years}$ , effective assessment of pipe condition is required to plan renewal programs for distribution systems. The paper is based on a project for the American Water Works Association Research Foundation and presents findings and a management framework about utility practices in condition assessment and it validates the framework through comparisons with practices in leading utilities. The project findings indicated that utilities can utilize available information much better than they do. However, they are impeded by lack of a standard procedure for recording data on leaks, breaks, and condition indicators. Ideally, the large amount of information involved would be consolidated into a composite condition index, but this is not feasible. Advanced applications are required for the future. These might include real-time assessment, smart pigs to collect data, small chip sets, and automated pipe data registration. Research needs are indicated in nondestructive testing, leak detection and loss measurement, causes for pipe, joint, lining, and coating deterioration, and in situ methods to test condition. While condition assessment is a promising concept, it is not applied consistently. In the past, condition information was used by operations and engineering departments. The same decisions still require condition information, but organizations are more integrated, have newer methods available, and face higher standards and expectations than in the past. When utilities manage their distribution capital programs better, it can lead to organizational improvement across the board because different departments can work together to improve the entire system. By use of the proposed framework, combined with effective information systems, operations improvements, and capital improvement programs, utilities can optimize returns on their investments in distribution systems. Utilities will continue to face obstacles such as lack of records, inability to inspect pipes while in service, and not being supported for funding by boards or councils. In the end, the solutions may already be within the grasp of utilities, through better management of existing information.

For coastal infrastructure the long-term durability of new steel structures and the remaining life of existing steel structures is of central interest to their proper maintenance and asset management. This is particularly the case where protective measures such as paint coatings, galvanizing, or cathodic protection will become or are already ineffective or are nonexistent. Guidelines currently available for new design and for assessment are largely empirical and have a high degree of uncertainty. To address this recent research has produced better quality models for the progression of corrosion with time. These employ fundamental characteristics of steel corrosion as obtained from actual field observations and from laboratory-based electrochemical and other observations. The models are reviewed herein and an illustrative application to a typical infrastructure component is presented. It is shown that seawater temperature has an important influence on the rate of early corrosion and also has longer-term effects not predicted by short-term observations. Also, the influence on corrosion of small changes in metal composition and in water velocity, salinity, and pollution are described.

This paper considers the nature of repeated measurement data under the framework of the mixed-effects approach with an extension of an empirical nonlinear model of rutting progression on asphalt concrete pavements. The parameters estimates using the mixed-effects approach, which allow some parameters to vary randomly, are found to differ significantly from ordinary least-squares (OLS) and random-effects (RE) estimates. OLS ignores unobserved heterogeneity whereas RE attempts to capture it through the model’s constant term. However, unobserved pavement sections’ heterogeneity may manifest not only through model intercepts but through other model parameters as well, which makes the mixed-effects approach more appropriate for the analysis. The results obtained with the mixed-effects approach are more in accordance with a priori expectations. The paper also presents important modifications to the specification of an empirical rutting model that deals with different load magnitudes. The modified specification is found to be appropriate for modeling material hardening with loading. The results point out a potential problem with the traditional approach of dealing with mixed traffic loads in the estimation of models for predicting rutting progression.

Sewer rehabilitation is one control option for relieving wet-weather sanitary sewer overflows by reducing rainfall dependent inflow and infiltration. The prioritization of rehabilitation activities is based on the physical condition of a collection system, which is usually only partially known. Linear regression methods have been used to estimate the condition of the unobserved set of pipes based on relationships derived from the observed set of pipes. This method can provide unsatisfactory relationships between the sewer condition and measured independent variables, resulting in highly uncertain rehabilitation cost estimates. Discriminant analysis has been effective at weighing the costs of misclassifying pipes and deriving least-cost classification rules. Classification rules may be derived within a data-mining framework using evolutionary algorithms or with logistic regression methods. The estimated spatial distribution of deficient pipes may then be refined on an aggregated scale with a bootstrap estimate of classification error. The result is a screening and prioritization tool for providing cost estimates for rehabilitation and replacement activities. This method is demonstrated with an 8,919 pipe sanitary sewer system in Vallejo, Calif.

Many municipal water distribution systems across North America are reaching or have exceeded their design lives and, therefore, require extensive upgrading through rehabilitation and/or replacement. However, these needs far surpass the available resources, and decision makers must prioritize their replacement/rehabilitation needs. One such approach is the determination of the optimal replacement time based on the minimization of the total or annual average cost during a predetermined service period. This paper describes a simple approach for the optimization of replacement/rehabilitation activities for a network of buried pipes, which is based on the assumption that the occurrence of breaks in a pipeline segment follows a nonhomogeneous Poisson process. Equations for evaluating the optimal replacement time are derived by minimizing the expected annual average cost during the service period of the pipeline segment. Predictions are compared with those obtained by minimizing the expected total (accumulated) cost during the service or planning period. The use of the proposed approach is illustrated via numerical examples. Optimal replacement time predictions, based on minimization of the annual average cost, were found to be significantly longer than those obtained based on minimization of the total cost for the case—where break occurrence rate follows an exponential function.

We present an application of systematic probing for selecting optimal maintenance, repair, and reconstruction (MR&R) policies for systems of infrastructure facilities under model uncertainty. We use an open-loop feedback control approach, where the model parameters are updated sequentially after every inspection round. The use of systematic probing improves the convergence of the model parameters by ensuring that all permissible actions are applied to every condition state. The results of the parametric analyses demonstrate that the MR&R policies converge earlier when systematic probing is used. However, the savings in the expected total costs as a result of probing are minor, and are only realized when the optimal probing fractions are used. On the other hand, the additional costs incurred when the wrong probing fractions are used are significant. The major conclusion from this work is that state-of-the-art adaptive infrastructure management systems, that do not use probing, provide sufficiently close to optimal policies.